Robust Standard Errors in Small Samples: Some Practical Advice
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
| Authors | Guido Imbens, M. Kolesár |
| Journal/Conference Name | Review of Economics and Statistics |
| Paper Category | Social Sciences |
| Paper Abstract | We study the properties of heteroskedasticity-robust confidence intervals for regression parameters. We show that confidence intervals based on a degrees-of-freedom correction suggested by Bell and McCaffrey (2002) are a natural extension of a principled approach to the Behrens-Fisher problem. We suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for samples with fifty or more clusters.We recommend that researchers routinely calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors. |
| Date of publication | 2012 |
| Code Programming Language | R |
| Comment |
Copyright Researcher 2022